CLI tool for analyzing, evaluating, and optimizing LLM prompts used by developers.
- Target: Claude, OpenAI, Gemini, Cursor IDE/CLI, Codex CLI, Bedrock/Vertex/Local LLM CLI workflows
- Goal: Measure accuracy, reproducibility, and cost, with automatic refactoring suggestions
- Philosophy: Local-first, Contract-first (OpenAPI/JSONSchema), Reproducible reports
- License: Apache License 2.0
🇰🇷 Korean Documentation: Korean README | Korean User Docs
- Static Prompt Analysis: Section structure, duplicate rules, forbidden words, schema presence
- Dynamic Evaluation (Optional): Multi-sample → Schema fit rate, self-consistency, latency, cost
- Scoring: 0–100 overall score + sub-metrics
- Refactoring Suggestions: Token reduction, rule separation, few-shot summarization, cache optimization
- Report Output: Terminal rich summary by default (<=100 lines). Optional file output via
--outputand--single-output.
The new Docusaurus site lives under website/ and mirrors the English/Korean guides from README*.md and docs/public/**.
Run it locally:
cd website
npm install
npm run startCI builds the site via .github/workflows/docs.yml. Adjust docusaurus.config.ts once you select the final production URL (e.g., GitHub Pages).
make build
./bin/curompt --help# 기본: 프로젝트 스캔 → 배치 수집 → 병렬 평가 → 리치 요약(stdout)
./bin/curompt scan
# 하위 점수 Top-20만 보고 싶을 때
./bin/curompt scan --top 20
# 전체 리포트를 콘솔로 확인
./bin/curompt scan --full
# 파일 저장(단일 파일 병합)
./bin/curompt scan --output reports --single-output all_reports.md
# 병렬 작업 수 조정(예: 8)
./bin/curompt scan --concurrency 8Note:
scananalyzes prompts already stored in the local DB (saved viacurompt collect …orcurompt eval …).
If the selected path has no history yet, the CLI will offer to auto-collect from Claude Code or Codex logs (Cursor support ships in v1.1).
Use--path /absolute/project/pathto filter by project directory.
# Automated test script (build to test)
./test-quick.shThis script automatically:
- ✅ Checks build
- ✅ Creates test prompt files
- ✅ Tests scan command and verifies rich summary
- ✅ Verifies optional report generation
📖 Detailed guide: See Getting Started Guide (step-by-step instructions, examples, troubleshooting)
- Go ≥ 1.23
- macOS/Linux
- Optional: Python3 (for plugins, future)
The easiest way to install curompt on macOS:
# Add tap
brew tap curogom/curompt
# Install
brew install curompt
# Verify installation
curompt --versionUpdate:
brew upgrade curompt✅ Available now: Homebrew tap is ready! You can install
curomptusing the commands above.
Install directly using Go modules to $GOPATH/bin or ~/go/bin:
# Clone repository (or if already cloned)
git clone https://github.com/curogom/curompt.git
cd curompt
# Install
make install
# Or install directly
go install github.com/curogom/curompt/cmd/curompt@latestPATH verification and setup:
# Check Go bin path
go env GOPATH
# Output example: /Users/username/go
# Add to PATH (may already be there)
export PATH="$PATH:$(go env GOPATH)/bin"
# Verify
which curompt
curompt --helpPermanently add to PATH (add to shell config file):
# zsh users (~/.zshrc)
echo 'export PATH="$PATH:$(go env GOPATH)/bin"' >> ~/.zshrc
source ~/.zshrc
# bash users (~/.bashrc or ~/.bash_profile)
echo 'export PATH="$PATH:$(go env GOPATH)/bin"' >> ~/.bashrc
source ~/.bashrc# Build
make build
# Run (without PATH)
./bin/curompt --help
# Or manually add to PATH
sudo cp ./bin/curompt /usr/local/bin/
# Or
cp ./bin/curompt ~/bin/
export PATH="$PATH:~/bin"# Check version
curompt --version
# Check help
curompt --help
# Basic test (rich summary)
curompt scan --top 5📖 Detailed installation guide: See Installation Guide (PATH setup, troubleshooting included)
# Build only
make build
# Run tests
make test
# Check test coverage
make coverage
# All checks (format, lint, test)
make check- Claude: Anthropic API integration (token counting, metadata)
- OpenAI: OpenAI API integration (planned)
- Gemini: Google Gemini API integration (planned)
- Cursor IDE/CLI: Prompt capture via CLI wrapper
- Codex CLI: Prompt capture via CLI wrapper
- Bedrock/Vertex/Local: Standard input/output wrapping support
Note: In MVP, LLM Providers only provide metadata (token counting, cost estimation). Actual API calls will be implemented in M2.
- Local-first: Runs locally by default, no external transmission
- Auto masking: Automatic masking of API keys/tokens/emails/URL queries/.env references
- Local storage: SQLite storage only (
~/.curompt/db.sqlite)
For details, see SECURITY.md.
Modular architecture ensures scalability and maintainability:
- Module separation: Clear separation of responsibilities (Collector, Parser, Analyzer, Scorer, Provider, etc.)
- Design patterns: Strategy, Repository, Dependency Injection patterns applied
- Test strategy: Unit tests (85% coverage) + Integration tests
For detailed architecture, see ARCHITECTURE.md.
make testgo test ./test/integration/... -vmake coverageCurrent coverage: Average 85% (Target 80% achieved)
- Installation Guide: System installation and PATH setup ⭐
- Getting Started Guide: Hands-on testing guide ⭐
- Architecture: Module structure and design patterns
- Roadmap: Milestones and feature plans
- Tech Stack: Technologies used and selection rationale
- Config Guide: YAML configuration file guide
- Metrics: Scoring metrics explanation
- Provider Support: Supported LLM Provider list
- Security: Security and privacy policy
All user documentation is available in
docs/public/.
This project is distributed under the Apache License 2.0. Contributions are welcome!
Apache License 2.0
For details, see LICENSE.